Main network model diagnostics - balanced statistics
This file shows diagnostics for main network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.m.buildup.bal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 | 2240.5 |
| nodefactor.deg.pers.1 | NA | NA | NA | 493.0 | 493.0 | 493.0 | 493.0 | 493.0 |
| nodefactor.deg.pers.2 | NA | NA | NA | 603.0 | 603.0 | 603.0 | 603.0 | 603.0 |
| nodefactor.race..wa.B | NA | 213.8 | 213.8 | 213.8 | 213.8 | 213.8 | 213.8 | 213.8 |
| nodefactor.race..wa.H | NA | 587.8 | 587.8 | 587.8 | 587.8 | 587.8 | 587.8 | 587.8 |
| nodefactor.region.EW | NA | NA | NA | NA | 445.6 | 445.6 | 445.6 | 445.6 |
| nodefactor.region.OW | NA | NA | NA | NA | 1278.1 | 1278.1 | 1278.1 | 1278.1 |
| nodematch.race..wa.B | NA | NA | 31.2 | 31.2 | 31.2 | 31.2 | 31.2 | 31.2 |
| nodematch.race..wa.H | NA | NA | 123.3 | 123.3 | 123.3 | 123.3 | 123.3 | 123.3 |
| nodematch.race..wa.O | NA | NA | 1638.9 | 1638.9 | 1638.9 | 1638.9 | 1638.9 | 1638.9 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1206.3 | 1206.3 | 1206.3 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| mix.region.EW.KC | NA | NA | NA | NA | NA | NA | -Inf | NA |
| mix.region.EW.OW | NA | NA | NA | NA | NA | NA | -Inf | NA |
| mix.region.KC.OW | NA | NA | NA | NA | NA | NA | -Inf | NA |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 2016.5 |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 0.04827 29.26193 0.16894 0.17268
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -57.5 -19.5 0.5 19.5 57.5
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.000812571
## Lag 2e+05 -0.014008792
## Lag 3e+05 -0.013181798
## Lag 4e+05 -0.024329088
## Lag 5e+05 -0.004715004
## Chain 2
## edges
## Lag 0 1.0000000000
## Lag 1e+05 0.0113871359
## Lag 2e+05 0.0051766637
## Lag 3e+05 0.0095815335
## Lag 4e+05 0.0018761518
## Lag 5e+05 -0.0007773829
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.007762176
## Lag 2e+05 0.028607313
## Lag 3e+05 -0.007422592
## Lag 4e+05 -0.012009726
## Lag 5e+05 -0.016179124
## Chain 4
## edges
## Lag 0 1.00000000
## Lag 1e+05 0.01722746
## Lag 2e+05 0.02051297
## Lag 3e+05 0.00415593
## Lag 4e+05 0.01637713
## Lag 5e+05 0.01789402
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.8729
##
## Individual P-values (lower = worse):
## edges
## 0.3827018
## Joint P-value (lower = worse): 0.3787341 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.239
##
## Individual P-values (lower = worse):
## edges
## 0.2151753
## Joint P-value (lower = worse): 0.2518375 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.7333
##
## Individual P-values (lower = worse):
## edges
## 0.4633587
## Joint P-value (lower = worse): 0.4428171 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.4826
##
## Individual P-values (lower = worse):
## edges
## 0.6293818
## Joint P-value (lower = worse): 0.6262199 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.16140 28.89 0.16677 0.16761
## nodefactor.race..wa.B -0.35277 11.93 0.06889 0.06860
## nodefactor.race..wa.H -0.03357 16.51 0.09535 0.09635
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.50 -19.500 0.5000 19.500 56.50
## nodefactor.race..wa.B -23.83 -8.834 0.1664 7.166 23.17
## nodefactor.race..wa.H -31.84 -10.844 0.1560 11.156 32.16
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.27886691
## nodefactor.race..wa.B 0.2788669 1.00000000
## nodefactor.race..wa.H 0.3788393 0.02229181
## nodefactor.race..wa.H
## edges 0.37883930
## nodefactor.race..wa.B 0.02229181
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0195321964 0.006087381 0.013009692
## Lag 2e+05 -0.0053867865 0.020550132 -0.004257186
## Lag 3e+05 0.0007608276 0.001542963 -0.005080063
## Lag 4e+05 0.0139952676 -0.008069587 -0.002986683
## Lag 5e+05 -0.0049831999 -0.011553817 -0.011021465
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.006972719 0.0001342158 0.022476011
## Lag 2e+05 -0.002133960 0.0056226991 -0.007535108
## Lag 3e+05 -0.005716569 -0.0223128768 0.009045639
## Lag 4e+05 0.014376243 -0.0213538759 0.007926069
## Lag 5e+05 -0.002807807 0.0074446690 0.001725296
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006531002 -0.016808133 -0.003807935
## Lag 2e+05 0.008894848 0.001996580 -0.012241177
## Lag 3e+05 -0.005820613 0.007095523 -0.001577152
## Lag 4e+05 -0.008469688 -0.003821740 -0.022402398
## Lag 5e+05 -0.007095829 0.015782163 -0.005541702
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 2.515585e-03 0.008988164 0.019596458
## Lag 2e+05 1.723743e-02 -0.012986270 -0.015809080
## Lag 3e+05 -1.198866e-02 0.010389819 0.006600693
## Lag 4e+05 -1.834574e-05 0.015947977 -0.010012109
## Lag 5e+05 -7.682528e-03 0.003210243 -0.004021384
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.2078 -1.3891 0.1263
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2271064 0.1647872 0.8995327
## Joint P-value (lower = worse): 0.1626046 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.0034 -1.8141 -0.9348
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.31565589 0.06965786 0.34990654
## Joint P-value (lower = worse): 0.2638284 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5860 -1.6830 -0.7085
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.55789177 0.09238176 0.47865467
## Joint P-value (lower = worse): 0.3624487 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.3623 1.1003 0.2287
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7171220 0.2711948 0.8191352
## Joint P-value (lower = worse): 0.5859225 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.04597 29.034 0.16763 0.16763
## nodefactor.race..wa.B 0.10420 12.699 0.07332 0.07490
## nodefactor.race..wa.H 0.29213 17.610 0.10167 0.10904
## nodematch.race..wa.B 0.11116 4.981 0.02876 0.03089
## nodematch.race..wa.H 0.27055 8.757 0.05056 0.06556
## nodematch.race..wa.O -0.22163 26.416 0.15251 0.15232
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.500 -19.500 -0.50000 19.500 56.500
## nodefactor.race..wa.B -24.834 -8.834 0.16640 8.166 25.166
## nodefactor.race..wa.H -33.844 -11.844 0.15600 12.156 35.156
## nodematch.race..wa.B -9.177 -3.177 -0.17694 3.823 9.823
## nodematch.race..wa.H -16.300 -5.300 0.69972 5.700 17.700
## nodematch.race..wa.O -51.946 -17.946 0.05397 17.054 52.054
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.26341218
## nodefactor.race..wa.B 0.2634122 1.00000000
## nodefactor.race..wa.H 0.3600299 0.04743928
## nodematch.race..wa.B 0.1125557 0.57542603
## nodematch.race..wa.H 0.1304341 -0.04962059
## nodematch.race..wa.O 0.8191997 -0.05277563
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.36002992 0.11255571
## nodefactor.race..wa.B 0.04743928 0.57542603
## nodefactor.race..wa.H 1.00000000 -0.01750918
## nodematch.race..wa.B -0.01750918 1.00000000
## nodematch.race..wa.H 0.59476949 0.01125704
## nodematch.race..wa.O -0.06283077 0.02212869
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.13043412 0.81919975
## nodefactor.race..wa.B -0.04962059 -0.05277563
## nodefactor.race..wa.H 0.59476949 -0.06283077
## nodematch.race..wa.B 0.01125704 0.02212869
## nodematch.race..wa.H 1.00000000 0.07138299
## nodematch.race..wa.O 0.07138299 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.011208103 0.029789555 0.054696518
## Lag 2e+05 0.005052589 -0.010114664 0.014285503
## Lag 3e+05 0.002312177 -0.007342714 -0.023233439
## Lag 4e+05 0.004227302 0.014950665 -0.020507712
## Lag 5e+05 0.003788233 0.003250517 -0.002222634
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.052004416 0.242352007 0.0116599475
## Lag 2e+05 -0.011522050 0.064701631 -0.0033268290
## Lag 3e+05 -0.003198173 0.004514173 0.0175037043
## Lag 4e+05 0.001351352 -0.006320866 0.0021363092
## Lag 5e+05 -0.021783436 -0.013148267 0.0000869757
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008315285 0.023384221 0.076628361
## Lag 2e+05 0.005919698 -0.009589067 0.030624057
## Lag 3e+05 -0.004743854 -0.007090496 -0.005666997
## Lag 4e+05 -0.020476812 -0.003349672 0.012568118
## Lag 5e+05 0.009085159 0.013542092 -0.002743474
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.053192246 0.2640043133 -0.007635212
## Lag 2e+05 0.010216815 0.0819883136 0.011403395
## Lag 3e+05 0.018601429 0.0120562369 0.005709449
## Lag 4e+05 0.005380062 0.0234939229 -0.011390689
## Lag 5e+05 0.027208215 0.0005846989 0.013501395
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0025697584 0.032638850 0.074429674
## Lag 2e+05 -0.0003722483 0.011145369 0.010129884
## Lag 3e+05 0.0005192685 0.002223597 0.021366519
## Lag 4e+05 0.0112559960 -0.007507226 -0.006198121
## Lag 5e+05 0.0014807265 0.004903909 -0.001255750
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.076422422 0.247745845 -0.016008765
## Lag 2e+05 0.013355573 0.075227808 0.003136806
## Lag 3e+05 0.022249190 0.018094273 0.004314354
## Lag 4e+05 -0.006144743 -0.002030739 0.012968243
## Lag 5e+05 -0.007460851 0.001254804 -0.007126456
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005985276 0.007950551 0.075829176
## Lag 2e+05 0.015745851 0.005858133 0.012460052
## Lag 3e+05 -0.003117628 0.006828756 0.002508038
## Lag 4e+05 -0.009449592 -0.011924740 0.004849127
## Lag 5e+05 0.002574178 -0.014319588 -0.005092043
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.047420692 0.255995352 -0.007210961
## Lag 2e+05 0.006274315 0.046861111 0.020456257
## Lag 3e+05 0.015714298 0.004293817 -0.018927599
## Lag 4e+05 -0.011147433 0.016919669 0.001546832
## Lag 5e+05 -0.008059116 0.010642682 0.011775199
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.9958 1.8304 0.4029
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1850 -0.6769 1.2596
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.04595596 0.06719454 0.68699311
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.85320383 0.49849653 0.20782123
## Joint P-value (lower = worse): 0.2286995 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.4395 0.7850 -1.8789
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9023 -2.3143 0.3429
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.66030447 0.43247516 0.06026413
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.36691689 0.02064973 0.73164045
## Joint P-value (lower = worse): 0.1785632 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2101 -1.5322 0.7221
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.2804 0.1064 0.2287
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8336191 0.1254656 0.4702420
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7791342 0.9152534 0.8191114
## Joint P-value (lower = worse): 0.6427681 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.84547 -1.86946 0.08127
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.43537 -0.15651 1.75705
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.39785073 0.06155932 0.93522395
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.15118048 0.87563043 0.07890863
## Joint P-value (lower = worse): 0.1701983 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.1409 29.135 0.16821 0.16821
## nodefactor.deg.pers.1 0.3364 17.719 0.10230 0.10019
## nodefactor.deg.pers.2 -0.5685 18.867 0.10893 0.11298
## nodefactor.race..wa.B 0.3244 12.728 0.07348 0.07535
## nodefactor.race..wa.H 1.4040 17.540 0.10127 0.10912
## nodematch.race..wa.B 0.1903 5.069 0.02926 0.03154
## nodematch.race..wa.H 0.5690 8.759 0.05057 0.06619
## nodematch.race..wa.O -0.1223 26.512 0.15307 0.15307
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -55.500 -18.500 1.500e+00 20.500 58.50
## nodefactor.deg.pers.1 -34.000 -11.000 5.684e-14 12.000 35.00
## nodefactor.deg.pers.2 -38.000 -13.000 -1.000e+00 12.000 36.00
## nodefactor.race..wa.B -24.834 -7.834 1.664e-01 9.166 25.17
## nodefactor.race..wa.H -32.844 -9.844 1.156e+00 13.156 35.16
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 10.82
## nodematch.race..wa.H -16.300 -5.300 6.997e-01 6.700 17.70
## nodematch.race..wa.O -50.946 -17.946 5.397e-02 17.054 52.05
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.40132066
## nodefactor.deg.pers.1 0.4013207 1.00000000
## nodefactor.deg.pers.2 0.4227455 0.05234482
## nodefactor.race..wa.B 0.2702926 0.10979860
## nodefactor.race..wa.H 0.3606214 0.15682145
## nodematch.race..wa.B 0.1157809 0.04630690
## nodematch.race..wa.H 0.1372652 0.05534839
## nodematch.race..wa.O 0.8226218 0.32384511
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42274555 0.27029260
## nodefactor.deg.pers.1 0.05234482 0.10979860
## nodefactor.deg.pers.2 1.00000000 0.13133040
## nodefactor.race..wa.B 0.13133040 1.00000000
## nodefactor.race..wa.H 0.17403882 0.04682189
## nodematch.race..wa.B 0.06432286 0.57418114
## nodematch.race..wa.H 0.07169058 -0.03696470
## nodematch.race..wa.O 0.33529678 -0.03963260
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.36062137 0.11578086
## nodefactor.deg.pers.1 0.15682145 0.04630690
## nodefactor.deg.pers.2 0.17403882 0.06432286
## nodefactor.race..wa.B 0.04682189 0.57418114
## nodefactor.race..wa.H 1.00000000 -0.01569720
## nodematch.race..wa.B -0.01569720 1.00000000
## nodematch.race..wa.H 0.59798072 0.02798280
## nodematch.race..wa.O -0.05944949 0.02943175
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.13726524 0.82262182
## nodefactor.deg.pers.1 0.05534839 0.32384511
## nodefactor.deg.pers.2 0.07169058 0.33529678
## nodefactor.race..wa.B -0.03696470 -0.03963260
## nodefactor.race..wa.H 0.59798072 -0.05944949
## nodematch.race..wa.B 0.02798280 0.02943175
## nodematch.race..wa.H 1.00000000 0.07249846
## nodematch.race..wa.O 0.07249846 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.002745247 -0.0005698549 0.005736073
## Lag 2e+05 -0.016436802 -0.0038128123 -0.018760251
## Lag 3e+05 0.011433790 0.0013849737 -0.014862439
## Lag 4e+05 0.008482136 -0.0195622978 -0.011919800
## Lag 5e+05 0.019341633 0.0016227486 0.001014457
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.020136029 0.071397656 0.067273758
## Lag 2e+05 -0.009847821 0.033410295 0.003976353
## Lag 3e+05 -0.000639021 0.003845062 -0.003821269
## Lag 4e+05 0.009923656 -0.007287995 -0.001324894
## Lag 5e+05 -0.011802051 0.001544721 0.003124793
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.26893548 0.003606402
## Lag 2e+05 0.07623866 -0.007742246
## Lag 3e+05 0.02484582 0.008752092
## Lag 4e+05 0.02287966 -0.007704242
## Lag 5e+05 0.02220601 0.017808442
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.003163241 0.007325812 0.0149381762
## Lag 2e+05 -0.001253138 -0.002291310 -0.0016429226
## Lag 3e+05 0.005139615 0.008733096 0.0021712699
## Lag 4e+05 -0.020638224 -0.024516227 -0.0003938711
## Lag 5e+05 0.008635419 -0.005315609 0.0368029299
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0317485820 0.0725305787 0.081356012
## Lag 2e+05 -0.0066859368 0.0057115100 0.017594969
## Lag 3e+05 -0.0189778087 -0.0001302909 -0.007434138
## Lag 4e+05 0.0002408441 -0.0033021870 0.002455499
## Lag 5e+05 -0.0230510572 0.0015200933 -0.004694521
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.253929656 0.0007777483
## Lag 2e+05 0.050732118 -0.0048771621
## Lag 3e+05 0.004803744 0.0094859891
## Lag 4e+05 0.004749943 -0.0110967666
## Lag 5e+05 -0.005699775 0.0083651479
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003360710 0.008422362 0.006027530
## Lag 2e+05 0.006978230 0.005955221 -0.015985002
## Lag 3e+05 -0.024733098 0.001614016 -0.014265209
## Lag 4e+05 0.005719774 -0.005355170 0.025552569
## Lag 5e+05 0.016981669 0.005586289 0.007752374
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.019272486 0.043146518 0.0772420620
## Lag 2e+05 0.003247312 0.017050074 0.0206703658
## Lag 3e+05 -0.024403018 0.010747317 -0.0020094039
## Lag 4e+05 0.001187666 -0.004010833 0.0004618976
## Lag 5e+05 -0.011393580 0.012837439 -0.0035139164
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.255154172 0.005804222
## Lag 2e+05 0.098559213 -0.004915787
## Lag 3e+05 0.037375858 0.002325456
## Lag 4e+05 0.017349115 0.015524794
## Lag 5e+05 0.005757827 0.022083184
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.0107561813 -0.0005184371 0.0270551510
## Lag 2e+05 -0.0004782635 -0.0213638452 0.0009989092
## Lag 3e+05 0.0031789721 -0.0194695799 0.0170358783
## Lag 4e+05 0.0074754519 -0.0330261535 0.0198503988
## Lag 5e+05 0.0044960120 0.0115033137 0.0248903117
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.030638882 0.04278515 0.072893477
## Lag 2e+05 0.018668703 0.03889139 0.008819404
## Lag 3e+05 -0.005133513 0.01089026 0.008439988
## Lag 4e+05 -0.004081987 -0.00390614 0.004266348
## Lag 5e+05 0.003129103 -0.01273688 0.016858548
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.2375936227 0.007978804
## Lag 2e+05 0.0579818834 -0.003135887
## Lag 3e+05 0.0149737602 -0.004675465
## Lag 4e+05 -0.0004692792 0.007767258
## Lag 5e+05 -0.0164553055 0.005543742
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6597 0.8859 0.2569
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.4230 -1.8419 -0.5330
## nodematch.race..wa.H nodematch.race..wa.O
## -1.5012 1.4016
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.50943011 0.37565741 0.79727829
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.67230278 0.06549209 0.59405953
## nodematch.race..wa.H nodematch.race..wa.O
## 0.13330922 0.16104964
## Joint P-value (lower = worse): 0.4008152 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.4684 -0.4828 2.0007
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.3025 1.5219 2.0254
## nodematch.race..wa.H nodematch.race..wa.O
## 1.4606 1.3057
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.14198484 0.62926000 0.04542308
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.76226699 0.12802286 0.04282867
## nodematch.race..wa.H nodematch.race..wa.O
## 0.14412659 0.19165580
## Joint P-value (lower = worse): 0.1298688 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.35143 -1.88365 1.06622
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.40138 1.02980 -0.09193
## nodematch.race..wa.H nodematch.race..wa.O
## 0.52247 -0.69290
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.72526297 0.05961224 0.28632547
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.68814021 0.30310438 0.92675117
## nodematch.race..wa.H nodematch.race..wa.O
## 0.60134049 0.48837170
## Joint P-value (lower = worse): 0.5479493 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.87791 0.09086 0.70552
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -1.20918 -0.86620 0.86729
## nodematch.race..wa.H nodematch.race..wa.O
## 1.38674 0.46245
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.3799926 0.9276013 0.4804885
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.2265926 0.3863796 0.3857804
## nodematch.race..wa.H nodematch.race..wa.O
## 0.1655205 0.6437619
## Joint P-value (lower = worse): 0.1683648 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.30773 28.780 0.16616 0.16882
## nodefactor.deg.pers.1 -0.10860 17.687 0.10212 0.10212
## nodefactor.deg.pers.2 0.65457 18.613 0.10746 0.10706
## nodefactor.race..wa.B -0.02497 12.705 0.07335 0.07476
## nodefactor.race..wa.H -0.11593 17.477 0.10090 0.10925
## nodefactor.region.EW 0.25263 15.945 0.09206 0.09241
## nodefactor.region.OW 0.51080 29.500 0.17032 0.17027
## nodematch.race..wa.B -0.07311 5.014 0.02895 0.03112
## nodematch.race..wa.H -0.25268 8.743 0.05048 0.06592
## nodematch.race..wa.O 0.15164 26.418 0.15252 0.15432
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -56.500 -19.500 5.000e-01 19.500 56.500
## nodefactor.deg.pers.1 -35.000 -12.000 5.684e-14 12.000 35.000
## nodefactor.deg.pers.2 -36.000 -12.000 0.000e+00 13.000 37.025
## nodefactor.race..wa.B -24.834 -8.834 1.664e-01 8.166 25.166
## nodefactor.race..wa.H -34.844 -11.844 1.560e-01 11.156 34.156
## nodefactor.region.EW -30.561 -10.561 4.392e-01 11.439 31.439
## nodefactor.region.OW -57.131 -19.131 8.694e-01 19.869 58.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 2.823 9.823
## nodematch.race..wa.H -17.300 -6.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -51.946 -17.946 5.397e-02 18.054 52.054
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.40396471
## nodefactor.deg.pers.1 0.4039647 1.00000000
## nodefactor.deg.pers.2 0.4165221 0.04556771
## nodefactor.race..wa.B 0.2547034 0.11010520
## nodefactor.race..wa.H 0.3544556 0.16496778
## nodefactor.region.EW 0.3618646 0.14675518
## nodefactor.region.OW 0.6232773 0.23730057
## nodematch.race..wa.B 0.1060823 0.05009459
## nodematch.race..wa.H 0.1348891 0.06570727
## nodematch.race..wa.O 0.8188100 0.31943367
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.41652210 0.25470343
## nodefactor.deg.pers.1 0.04556771 0.11010520
## nodefactor.deg.pers.2 1.00000000 0.11326212
## nodefactor.race..wa.B 0.11326212 1.00000000
## nodefactor.race..wa.H 0.15764390 0.04568274
## nodefactor.region.EW 0.16224203 0.04554084
## nodefactor.region.OW 0.25688763 0.12612004
## nodematch.race..wa.B 0.04423138 0.57492530
## nodematch.race..wa.H 0.05852404 -0.03842465
## nodematch.race..wa.O 0.33153153 -0.06020016
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35445562 0.361864612
## nodefactor.deg.pers.1 0.16496778 0.146755181
## nodefactor.deg.pers.2 0.15764390 0.162242030
## nodefactor.race..wa.B 0.04568274 0.045540841
## nodefactor.race..wa.H 1.00000000 0.237324037
## nodefactor.region.EW 0.23732404 1.000000000
## nodefactor.region.OW 0.20011369 0.052898411
## nodematch.race..wa.B -0.01951699 0.004090968
## nodematch.race..wa.H 0.59195493 0.119488153
## nodematch.race..wa.O -0.06956484 0.264134522
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.62327731 0.106082278
## nodefactor.deg.pers.1 0.23730057 0.050094593
## nodefactor.deg.pers.2 0.25688763 0.044231382
## nodefactor.race..wa.B 0.12612004 0.574925296
## nodefactor.race..wa.H 0.20011369 -0.019516987
## nodefactor.region.EW 0.05289841 0.004090968
## nodefactor.region.OW 1.00000000 0.049680598
## nodematch.race..wa.B 0.04968060 1.000000000
## nodematch.race..wa.H 0.07131174 0.022399286
## nodematch.race..wa.O 0.52993717 0.018386733
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.13488906 0.81880999
## nodefactor.deg.pers.1 0.06570727 0.31943367
## nodefactor.deg.pers.2 0.05852404 0.33153153
## nodefactor.race..wa.B -0.03842465 -0.06020016
## nodefactor.race..wa.H 0.59195493 -0.06956484
## nodefactor.region.EW 0.11948815 0.26413452
## nodefactor.region.OW 0.07131174 0.52993717
## nodematch.race..wa.B 0.02239929 0.01838673
## nodematch.race..wa.H 1.00000000 0.07491606
## nodematch.race..wa.O 0.07491606 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008542287 0.002980698 -0.001923692
## Lag 2e+05 -0.009072005 0.009506289 0.001028801
## Lag 3e+05 0.014547358 -0.014283343 0.005353628
## Lag 4e+05 -0.007670068 -0.018298603 0.006005629
## Lag 5e+05 -0.004890381 0.009550699 -0.012004368
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.0208318802 0.0638244491 0.0123862384
## Lag 2e+05 -0.0128373565 0.0214285963 -0.0135528397
## Lag 3e+05 0.0115916808 0.0009343124 0.0110176195
## Lag 4e+05 0.0050129720 -0.0061646036 -0.0004921546
## Lag 5e+05 -0.0005475854 -0.0182401104 -0.0183672128
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018976014 0.097469770 0.254299214
## Lag 2e+05 0.022022062 -0.002958832 0.062433451
## Lag 3e+05 0.006651555 -0.005238543 0.007076696
## Lag 4e+05 0.002555040 0.005693267 0.011182032
## Lag 5e+05 -0.016968764 0.008590999 0.003805060
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.009284854
## Lag 2e+05 -0.004895938
## Lag 3e+05 0.017894637
## Lag 4e+05 0.004252024
## Lag 5e+05 -0.003601692
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012540947 0.013730624 0.030136490
## Lag 2e+05 -0.004804905 -0.001373605 -0.006692354
## Lag 3e+05 -0.006953238 0.001192709 0.004968828
## Lag 4e+05 -0.020140885 -0.008833628 -0.011103569
## Lag 5e+05 0.005872099 -0.003611982 0.003926455
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0038987016 0.078017331 0.037281231
## Lag 2e+05 -0.0032975842 0.020904930 0.011249512
## Lag 3e+05 0.0025644764 0.005038043 0.003342170
## Lag 4e+05 -0.0057375707 -0.004827892 -0.011970714
## Lag 5e+05 0.0007974622 0.010332627 -0.006141835
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009044004 0.057341055 0.252366737
## Lag 2e+05 0.019061559 -0.003967935 0.079405384
## Lag 3e+05 -0.003010424 0.004075459 0.023944863
## Lag 4e+05 -0.019026916 -0.011249797 0.019379553
## Lag 5e+05 -0.002962861 -0.007594571 0.007173682
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0101037678
## Lag 2e+05 0.0061156180
## Lag 3e+05 0.0067374804
## Lag 4e+05 -0.0198017091
## Lag 5e+05 0.0008365555
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003862936 -0.007734902 -0.005954799
## Lag 2e+05 0.033001452 0.004657990 -0.003215551
## Lag 3e+05 0.010096485 -0.009778390 -0.040095934
## Lag 4e+05 0.002495206 0.008946910 -0.022964576
## Lag 5e+05 0.028192475 0.022115349 0.004208261
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0109494472 0.063644821 0.014407612
## Lag 2e+05 -0.0142187949 0.024153762 0.005975160
## Lag 3e+05 0.0148181044 0.007620804 -0.010829447
## Lag 4e+05 -0.0006675172 0.013842050 0.003400889
## Lag 5e+05 0.0064483657 -0.001202179 -0.001259285
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.0002154924 0.074716651 0.27177817
## Lag 2e+05 0.0148608685 -0.011833517 0.08482142
## Lag 3e+05 0.0099464331 0.008370527 0.03873098
## Lag 4e+05 0.0082310620 -0.001350167 0.02513453
## Lag 5e+05 0.0127924170 -0.011160009 0.02036346
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0001284225
## Lag 2e+05 0.0188360287
## Lag 3e+05 0.0122445986
## Lag 4e+05 -0.0049103393
## Lag 5e+05 0.0357362603
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0135912469 -0.001684571 0.021632994
## Lag 2e+05 0.0105364432 -0.004941211 0.010307321
## Lag 3e+05 -0.0042666278 -0.003747846 -0.011002925
## Lag 4e+05 -0.0002665829 -0.007986214 -0.005327683
## Lag 5e+05 0.0055398314 0.015499904 -0.005590757
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.033573454 0.062566637 0.008869042
## Lag 2e+05 0.021078872 0.025947791 -0.016056179
## Lag 3e+05 -0.008178511 0.004778550 0.008485616
## Lag 4e+05 0.020617062 0.012945109 -0.025130480
## Lag 5e+05 -0.007265961 -0.000330355 -0.001663378
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.017741248 0.074562374 0.248381670
## Lag 2e+05 -0.007331964 0.003051506 0.063443280
## Lag 3e+05 0.003760935 -0.005910881 0.024090969
## Lag 4e+05 0.007215346 -0.003921630 0.013958284
## Lag 5e+05 -0.030226059 -0.004940401 -0.002631113
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 -0.0197973178
## Lag 2e+05 0.0004861966
## Lag 3e+05 -0.0062239339
## Lag 4e+05 0.0189977818
## Lag 5e+05 -0.0065658652
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.08054 1.75554 -0.93014
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.07170 0.12092 0.35507
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.81130 0.73966 -0.05306
## nodematch.race..wa.O
## 0.71775
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.27990281 0.07916769 0.35229774
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.28385460 0.90375165 0.72254060
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.41719450 0.45950724 0.95768467
## nodematch.race..wa.O
## 0.47291002
## Joint P-value (lower = worse): 0.8201166 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.47719 -1.05450 0.54443
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.26835 -0.20247 -0.34744
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.02862 -0.08727 -1.04427
## nodematch.race..wa.O
## 0.12505
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6332295 0.2916534 0.5861450
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.7884303 0.8395525 0.7282581
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.9771682 0.9304547 0.2963606
## nodematch.race..wa.O
## 0.9004860
## Joint P-value (lower = worse): 0.7460311 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.40033 -1.13108 -0.35552
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.57601 0.80942 0.01774
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.45095 0.84036 0.47417
## nodematch.race..wa.O
## -0.63682
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6889163 0.2580217 0.7222034
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1150227 0.4182748 0.9858476
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.1467937 0.4007068 0.6353768
## nodematch.race..wa.O
## 0.5242391
## Joint P-value (lower = worse): 0.5058335 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7484 0.3772 1.0303
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.4117 0.8763 0.6156
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.8934 2.1229 1.1259
## nodematch.race..wa.O
## -0.1002
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4542075 0.7060321 0.3028814
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.1580386 0.3808809 0.5381495
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.3716267 0.0337628 0.2602005
## nodematch.race..wa.O
## 0.9201931
## Joint P-value (lower = worse): 0.4846874 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.3362 28.655 0.16544 0.16702
## nodefactor.deg.pers.1 -0.4341 17.457 0.10079 0.10211
## nodefactor.deg.pers.2 0.1446 18.665 0.10776 0.11130
## nodefactor.race..wa.B -0.2209 12.626 0.07290 0.07902
## nodefactor.race..wa.H -0.0809 17.251 0.09960 0.12541
## nodefactor.region.EW 0.1399 16.051 0.09267 0.09872
## nodefactor.region.OW -0.2902 29.410 0.16980 0.17710
## nodematch.race..wa.B -0.1316 4.993 0.02882 0.03651
## nodematch.race..wa.H -0.1758 8.651 0.04995 0.08025
## nodematch.race..wa.O -0.2148 26.192 0.15122 0.15481
## absdiff.sqrt.age -0.1908 28.553 0.16485 0.16688
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -56.500 -19.500 -5.000e-01 19.500 56.500
## nodefactor.deg.pers.1 -35.000 -12.000 5.684e-14 11.000 34.000
## nodefactor.deg.pers.2 -36.000 -12.000 0.000e+00 13.000 37.000
## nodefactor.race..wa.B -24.834 -8.834 -8.336e-01 8.166 25.166
## nodefactor.race..wa.H -33.844 -11.844 1.560e-01 11.156 33.156
## nodefactor.region.EW -31.561 -10.561 4.392e-01 11.439 31.439
## nodefactor.region.OW -57.156 -20.131 -1.306e-01 19.869 57.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 2.823 9.823
## nodematch.race..wa.H -17.300 -6.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -50.946 -17.946 5.397e-02 17.054 51.054
## absdiff.sqrt.age -55.102 -19.759 -2.802e-01 19.326 55.985
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.40050192
## nodefactor.deg.pers.1 0.4005019 1.00000000
## nodefactor.deg.pers.2 0.4183869 0.04159989
## nodefactor.race..wa.B 0.2642528 0.10678321
## nodefactor.race..wa.H 0.3493018 0.15617993
## nodefactor.region.EW 0.3678966 0.14652313
## nodefactor.region.OW 0.6211976 0.23835336
## nodematch.race..wa.B 0.1082677 0.04757942
## nodematch.race..wa.H 0.1184630 0.05344195
## nodematch.race..wa.O 0.8219387 0.32337512
## absdiff.sqrt.age 0.5478571 0.22263633
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.41838689 0.26425277
## nodefactor.deg.pers.1 0.04159989 0.10678321
## nodefactor.deg.pers.2 1.00000000 0.12405573
## nodefactor.race..wa.B 0.12405573 1.00000000
## nodefactor.race..wa.H 0.16635599 0.03824666
## nodefactor.region.EW 0.15807638 0.04720871
## nodefactor.region.OW 0.25274689 0.13277548
## nodematch.race..wa.B 0.05043611 0.56936634
## nodematch.race..wa.H 0.06154356 -0.05195565
## nodematch.race..wa.O 0.33159568 -0.04661283
## absdiff.sqrt.age 0.21914700 0.14299754
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.34930178 0.36789662
## nodefactor.deg.pers.1 0.15617993 0.14652313
## nodefactor.deg.pers.2 0.16635599 0.15807638
## nodefactor.race..wa.B 0.03824666 0.04720871
## nodefactor.race..wa.H 1.00000000 0.23798291
## nodefactor.region.EW 0.23798291 1.00000000
## nodefactor.region.OW 0.20222363 0.05508460
## nodematch.race..wa.B -0.01793503 0.01239415
## nodematch.race..wa.H 0.59341279 0.12177764
## nodematch.race..wa.O -0.06875286 0.27388681
## absdiff.sqrt.age 0.19222431 0.20188113
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.62119761 0.10826773
## nodefactor.deg.pers.1 0.23835336 0.04757942
## nodefactor.deg.pers.2 0.25274689 0.05043611
## nodefactor.race..wa.B 0.13277548 0.56936634
## nodefactor.race..wa.H 0.20222363 -0.01793503
## nodefactor.region.EW 0.05508460 0.01239415
## nodefactor.region.OW 1.00000000 0.05226688
## nodematch.race..wa.B 0.05226688 1.00000000
## nodematch.race..wa.H 0.06943719 0.01124761
## nodematch.race..wa.O 0.52712791 0.01935570
## absdiff.sqrt.age 0.34312596 0.05798407
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.11846300 0.82193870
## nodefactor.deg.pers.1 0.05344195 0.32337512
## nodefactor.deg.pers.2 0.06154356 0.33159568
## nodefactor.race..wa.B -0.05195565 -0.04661283
## nodefactor.race..wa.H 0.59341279 -0.06875286
## nodefactor.region.EW 0.12177764 0.27388681
## nodefactor.region.OW 0.06943719 0.52712791
## nodematch.race..wa.B 0.01124761 0.01935570
## nodematch.race..wa.H 1.00000000 0.05971564
## nodematch.race..wa.O 0.05971564 1.00000000
## absdiff.sqrt.age 0.06557502 0.45041850
## absdiff.sqrt.age
## edges 0.54785710
## nodefactor.deg.pers.1 0.22263633
## nodefactor.deg.pers.2 0.21914700
## nodefactor.race..wa.B 0.14299754
## nodefactor.race..wa.H 0.19222431
## nodefactor.region.EW 0.20188113
## nodefactor.region.OW 0.34312596
## nodematch.race..wa.B 0.05798407
## nodematch.race..wa.H 0.06557502
## nodematch.race..wa.O 0.45041850
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.039202698 0.007405939 0.023281406
## Lag 2e+05 0.008849025 -0.016241057 -0.007577300
## Lag 3e+05 0.009454120 0.011486136 -0.002168793
## Lag 4e+05 -0.000657467 -0.001989726 0.028260015
## Lag 5e+05 0.002190588 -0.006470060 0.003226308
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.079319973 0.11608748 0.0318022430
## Lag 2e+05 0.017679860 0.06564404 0.0363968005
## Lag 3e+05 0.020526775 0.03970924 -0.0024831145
## Lag 4e+05 -0.006065356 0.02360628 0.0008340641
## Lag 5e+05 -0.004804690 0.02912619 0.0086500716
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.017453746 0.2209212668 0.38040379
## Lag 2e+05 0.008128531 0.0617716086 0.19338139
## Lag 3e+05 0.025604259 0.0114050369 0.10119218
## Lag 4e+05 -0.013240204 0.0007844741 0.06659826
## Lag 5e+05 -0.020024262 -0.0057973106 0.04366684
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.018983881 0.026557556
## Lag 2e+05 -0.005272583 0.014193569
## Lag 3e+05 0.004027528 0.006734619
## Lag 4e+05 0.003253652 0.009283374
## Lag 5e+05 -0.007907678 -0.001544995
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0078898745 0.028432815 0.027609495
## Lag 2e+05 0.0090811246 -0.005411520 0.029650546
## Lag 3e+05 -0.0007572838 -0.010038814 0.011420085
## Lag 4e+05 0.0262206879 0.005338408 -0.009432917
## Lag 5e+05 0.0075471619 0.010833606 -0.021532926
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.045893461 0.10976373 0.022179122
## Lag 2e+05 -0.010005613 0.06334136 0.021275726
## Lag 3e+05 -0.003425241 0.03131515 0.002323357
## Lag 4e+05 -0.004788523 0.03895106 0.004044513
## Lag 5e+05 -0.013860879 0.02538180 -0.008549236
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.017347717 0.198277945 0.37852164
## Lag 2e+05 0.021237604 0.059849669 0.19832981
## Lag 3e+05 0.005099190 0.016794947 0.10168638
## Lag 4e+05 -0.002910693 0.007856374 0.06690046
## Lag 5e+05 -0.005287947 0.014912908 0.04195017
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.018759377 0.013886888
## Lag 2e+05 0.015500809 0.004446377
## Lag 3e+05 -0.002887867 0.001593400
## Lag 4e+05 0.001572251 0.013226695
## Lag 5e+05 -0.009081279 -0.003021454
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.019725203 0.022900432 0.018809493
## Lag 2e+05 -0.007216561 0.010756420 -0.010814419
## Lag 3e+05 0.023719388 0.016496840 0.004405406
## Lag 4e+05 0.003679112 0.013443043 0.017629107
## Lag 5e+05 -0.011392314 0.008385525 -0.011035933
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0750644258 0.13367725 0.048554505
## Lag 2e+05 0.0291280462 0.05316322 0.004705089
## Lag 3e+05 0.0069559287 0.04823712 0.009972577
## Lag 4e+05 0.0135765204 0.02074794 0.031866319
## Lag 5e+05 -0.0007771856 0.03188090 -0.008587300
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.024425796 0.20222605 0.39837462
## Lag 2e+05 0.013746716 0.07333533 0.19537954
## Lag 3e+05 0.019487803 0.04153427 0.10507243
## Lag 4e+05 -0.005074453 0.01703641 0.07188453
## Lag 5e+05 -0.005113679 -0.01362445 0.04614958
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.003554326 -0.0006306819
## Lag 2e+05 0.008618389 0.0067487308
## Lag 3e+05 0.004473344 -0.0133906729
## Lag 4e+05 -0.006428943 -0.0050674595
## Lag 5e+05 -0.009561283 -0.0030457780
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0009841128 -0.01130665 0.014637155
## Lag 2e+05 0.0031935598 -0.01059705 0.010565625
## Lag 3e+05 0.0106141369 0.01963256 0.021235479
## Lag 4e+05 -0.0193698090 -0.01595087 0.008823975
## Lag 5e+05 0.0284708951 0.01006575 0.018021259
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0684207264 0.10751559 0.023631187
## Lag 2e+05 0.0319234240 0.04458401 0.026131589
## Lag 3e+05 -0.0009753907 0.03102584 -0.006319167
## Lag 4e+05 -0.0177870064 0.03682934 0.010573238
## Lag 5e+05 -0.0068226408 0.01377024 0.003874323
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.038106049 0.198210076 0.37514744
## Lag 2e+05 0.005911342 0.073822406 0.19022610
## Lag 3e+05 0.028541063 0.026117720 0.10473242
## Lag 4e+05 0.004222651 -0.004035867 0.05992105
## Lag 5e+05 0.016565473 0.006168649 0.03145967
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.004465693 0.021688135
## Lag 2e+05 0.001783250 -0.004907913
## Lag 3e+05 0.026146559 -0.003419824
## Lag 4e+05 -0.004399005 -0.007028599
## Lag 5e+05 0.026131602 0.011143464
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2090 0.3562 -0.8867
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.0885 -0.4525 -0.1697
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4274 0.3379 -1.1139
## nodematch.race..wa.O absdiff.sqrt.age
## -0.5515 0.9429
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.8344286 0.7217158 0.3752216
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.2763737 0.6508924 0.8652068
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6690743 0.7354059 0.2653077
## nodematch.race..wa.O absdiff.sqrt.age
## 0.5813154 0.3457085
## Joint P-value (lower = worse): 0.8507749 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.06739 -0.58502 -0.02166
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.25208 1.76780 0.72173
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.03252 0.28317 1.62998
## nodematch.race..wa.O absdiff.sqrt.age
## 0.35058 -1.31267
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.28579799 0.55853706 0.98271872
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.80098239 0.07709386 0.47046081
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.97405537 0.77704349 0.10310661
## nodematch.race..wa.O absdiff.sqrt.age
## 0.72590015 0.18929422
## Joint P-value (lower = worse): 0.3949642 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.66087 -0.03154 0.07853
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.67385 0.16334 0.02989
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.28603 -0.58260 -0.40245
## nodematch.race..wa.O absdiff.sqrt.age
## 0.81519 1.05444
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.09674034 0.97484059 0.93740335
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.09415921 0.87025360 0.97615249
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.19843303 0.56016035 0.68735224
## nodematch.race..wa.O absdiff.sqrt.age
## 0.41496159 0.29168302
## Joint P-value (lower = worse): 0.4327105 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.11764 0.53568 -0.54115
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 1.27794 -0.48613 -0.13664
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.49623 1.32883 -0.97710
## nodematch.race..wa.O absdiff.sqrt.age
## 0.08461 -1.31020
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.9063553 0.5921799 0.5884014
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.2012720 0.6268758 0.8913127
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6197329 0.1839041 0.3285193
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9325682 0.1901273
## Joint P-value (lower = worse): 0.5245738 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.3409 28.954 0.16716 0.20509
## nodefactor.deg.pers.1 0.4337 17.596 0.10159 0.11444
## nodefactor.deg.pers.2 0.0205 18.787 0.10847 0.13030
## nodefactor.race..wa.B -0.1459 12.554 0.07248 0.08937
## nodefactor.race..wa.H 0.8537 17.322 0.10001 0.17970
## nodefactor.region.EW 0.2217 18.288 0.10558 0.16969
## nodefactor.region.OW 0.6510 33.296 0.19223 0.20469
## nodematch.race..wa.B -0.1799 4.931 0.02847 0.04520
## nodematch.race..wa.H 0.1733 8.610 0.04971 0.13683
## nodematch.race..wa.O -0.3078 26.309 0.15190 0.17291
## absdiff.sqrt.age -0.1465 28.569 0.16494 0.17867
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -55.500 -19.500 5.000e-01 19.500 57.500
## nodefactor.deg.pers.1 -34.000 -11.000 5.684e-14 12.000 35.000
## nodefactor.deg.pers.2 -37.000 -13.000 0.000e+00 13.000 37.000
## nodefactor.race..wa.B -24.834 -8.834 1.664e-01 8.166 24.191
## nodefactor.race..wa.H -32.844 -10.844 1.156e+00 12.156 35.156
## nodefactor.region.EW -35.561 -11.561 4.392e-01 12.439 36.439
## nodefactor.region.OW -64.131 -22.131 -1.306e-01 23.869 65.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 2.823 9.823
## nodematch.race..wa.H -16.300 -5.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -51.946 -17.946 -9.460e-01 17.054 51.054
## absdiff.sqrt.age -55.139 -19.522 -4.877e-01 18.963 56.752
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39587037
## nodefactor.deg.pers.1 0.3958704 1.00000000
## nodefactor.deg.pers.2 0.4324802 0.04604631
## nodefactor.race..wa.B 0.2699487 0.10578419
## nodefactor.race..wa.H 0.3548353 0.16164935
## nodefactor.region.EW 0.3161591 0.13435631
## nodefactor.region.OW 0.5747786 0.20969378
## nodematch.race..wa.B 0.1041225 0.04363221
## nodematch.race..wa.H 0.1331056 0.07335396
## nodematch.race..wa.O 0.8228300 0.31832444
## absdiff.sqrt.age 0.5517659 0.21343431
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.43248025 0.26994868
## nodefactor.deg.pers.1 0.04604631 0.10578419
## nodefactor.deg.pers.2 1.00000000 0.13289750
## nodefactor.race..wa.B 0.13289750 1.00000000
## nodefactor.race..wa.H 0.15813675 0.04815951
## nodefactor.region.EW 0.14796254 0.03673300
## nodefactor.region.OW 0.24118703 0.11877553
## nodematch.race..wa.B 0.05976305 0.56013482
## nodematch.race..wa.H 0.05531873 -0.04273603
## nodematch.race..wa.O 0.34736867 -0.04290010
## absdiff.sqrt.age 0.23686395 0.15504950
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.35483532 0.316159143
## nodefactor.deg.pers.1 0.16164935 0.134356313
## nodefactor.deg.pers.2 0.15813675 0.147962541
## nodefactor.race..wa.B 0.04815951 0.036732997
## nodefactor.race..wa.H 1.00000000 0.224770402
## nodefactor.region.EW 0.22477040 1.000000000
## nodefactor.region.OW 0.19096190 -0.006425365
## nodematch.race..wa.B -0.01382299 0.013623290
## nodematch.race..wa.H 0.59690387 0.115037527
## nodematch.race..wa.O -0.06482711 0.227290315
## absdiff.sqrt.age 0.19812770 0.169504301
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.574778596 0.104122539
## nodefactor.deg.pers.1 0.209693779 0.043632214
## nodefactor.deg.pers.2 0.241187035 0.059763046
## nodefactor.race..wa.B 0.118775526 0.560134817
## nodefactor.race..wa.H 0.190961902 -0.013822993
## nodefactor.region.EW -0.006425365 0.013623290
## nodefactor.region.OW 1.000000000 0.033492389
## nodematch.race..wa.B 0.033492389 1.000000000
## nodematch.race..wa.H 0.069243414 0.007052211
## nodematch.race..wa.O 0.489451963 0.014272111
## absdiff.sqrt.age 0.319325303 0.056101096
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.133105552 0.82283002
## nodefactor.deg.pers.1 0.073353958 0.31832444
## nodefactor.deg.pers.2 0.055318733 0.34736867
## nodefactor.race..wa.B -0.042736032 -0.04290010
## nodefactor.race..wa.H 0.596903872 -0.06482711
## nodefactor.region.EW 0.115037527 0.22729032
## nodefactor.region.OW 0.069243414 0.48945196
## nodematch.race..wa.B 0.007052211 0.01427211
## nodematch.race..wa.H 1.000000000 0.06765965
## nodematch.race..wa.O 0.067659652 1.00000000
## absdiff.sqrt.age 0.073090025 0.45050529
## absdiff.sqrt.age
## edges 0.55176595
## nodefactor.deg.pers.1 0.21343431
## nodefactor.deg.pers.2 0.23686395
## nodefactor.race..wa.B 0.15504950
## nodefactor.race..wa.H 0.19812770
## nodefactor.region.EW 0.16950430
## nodefactor.region.OW 0.31932530
## nodematch.race..wa.B 0.05610110
## nodematch.race..wa.H 0.07309002
## nodematch.race..wa.O 0.45050529
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.08826008 0.103947566 0.10704295
## Lag 2e+05 0.03503032 0.018560359 0.03972203
## Lag 3e+05 0.03054123 0.009470079 0.04943772
## Lag 4e+05 0.02189859 0.010352221 0.02718131
## Lag 5e+05 0.01461508 0.029428779 0.01596672
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.118028260 0.28786662 0.32837545
## Lag 2e+05 0.050335796 0.16881826 0.17663378
## Lag 3e+05 0.037921167 0.13253545 0.10999496
## Lag 4e+05 0.041816160 0.08910375 0.06383326
## Lag 5e+05 0.007116593 0.05993399 0.03149067
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.048887292 0.39016659 0.6749795
## Lag 2e+05 0.010883240 0.18136615 0.5031622
## Lag 3e+05 0.001000355 0.11887792 0.3914737
## Lag 4e+05 0.004674848 0.08217766 0.3016400
## Lag 5e+05 0.014585707 0.04875591 0.2443631
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.088430011 0.0758183312
## Lag 2e+05 0.045131710 0.0336485313
## Lag 3e+05 0.022468001 0.0148307901
## Lag 4e+05 0.021031381 0.0009786120
## Lag 5e+05 0.009106743 -0.0005854615
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.078334705 0.09654722 0.11924434
## Lag 2e+05 0.043876068 0.03911747 0.05863247
## Lag 3e+05 0.029367331 0.02512674 0.03349835
## Lag 4e+05 -0.001398964 0.01349570 0.02964573
## Lag 5e+05 0.004757820 0.02119597 0.02799589
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.134166576 0.29499482 0.32474530
## Lag 2e+05 0.050257453 0.17924878 0.18537783
## Lag 3e+05 0.024493139 0.14105384 0.10899688
## Lag 4e+05 0.009509095 0.08968222 0.06078367
## Lag 5e+05 0.018946917 0.07475333 0.06045068
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.047777044 0.34932355 0.6657985
## Lag 2e+05 0.015904851 0.17192681 0.4914827
## Lag 3e+05 0.002498752 0.09539789 0.3721520
## Lag 4e+05 0.008040451 0.04346165 0.2877381
## Lag 5e+05 -0.006524582 0.02081922 0.2389953
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.086828290 0.055998143
## Lag 2e+05 0.059812064 0.014921365
## Lag 3e+05 0.027213278 0.009769602
## Lag 4e+05 0.004906727 -0.014839070
## Lag 5e+05 0.010804301 -0.005497228
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.068598157 0.090574353 0.1038017951
## Lag 2e+05 0.035277835 0.050342182 0.0462273400
## Lag 3e+05 0.023009524 0.019999030 0.0232689254
## Lag 4e+05 0.009775106 0.016609135 0.0061996542
## Lag 5e+05 -0.008529641 0.001941454 0.0002037566
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.147232131 0.27282434 0.31171121
## Lag 2e+05 0.047006967 0.17755842 0.15860930
## Lag 3e+05 0.029249513 0.11873364 0.08303764
## Lag 4e+05 0.031008926 0.09196339 0.06792686
## Lag 5e+05 0.005782397 0.08422395 0.05603013
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.00000000 1.0000000
## Lag 1e+05 0.0748416581 0.35939108 0.6628109
## Lag 2e+05 0.0038072322 0.16013384 0.4923825
## Lag 3e+05 -0.0016988172 0.09687858 0.3846767
## Lag 4e+05 0.0005295012 0.05966376 0.3098145
## Lag 5e+05 0.0121711277 0.02564017 0.2543966
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.075998145 0.062445629
## Lag 2e+05 0.038242493 0.019762294
## Lag 3e+05 0.022165940 0.007175586
## Lag 4e+05 0.008042402 -0.003784013
## Lag 5e+05 -0.011723425 0.001046195
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.0000000000 1.00000000
## Lag 1e+05 0.0716115758 0.0904722936 0.12801040
## Lag 2e+05 0.0293412587 0.0110793718 0.04990259
## Lag 3e+05 0.0205640434 0.0097471058 0.01885096
## Lag 4e+05 0.0204827130 0.0220562275 0.01091646
## Lag 5e+05 0.0006692579 0.0004111998 0.01335019
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.161623731 0.28408805 0.30930781
## Lag 2e+05 0.094821239 0.17671179 0.15268955
## Lag 3e+05 0.035337216 0.12588104 0.09408452
## Lag 4e+05 0.018497341 0.10969674 0.06068108
## Lag 5e+05 -0.009187217 0.09590119 0.05227518
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.059269793 0.373886675 0.6615745
## Lag 2e+05 0.022089126 0.183853153 0.4894539
## Lag 3e+05 -0.007940382 0.097552211 0.3824800
## Lag 4e+05 0.005960086 0.041712722 0.3127496
## Lag 5e+05 -0.001017895 0.002672131 0.2546519
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.082007134 0.058243964
## Lag 2e+05 0.030392459 0.039378110
## Lag 3e+05 0.009830594 0.011875120
## Lag 4e+05 0.015019353 0.018135648
## Lag 5e+05 0.003382568 -0.002697232
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.3681 0.9388 0.1340
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.4567 0.4803 1.4241
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.3031 0.8692 0.5920
## nodematch.race..wa.O absdiff.sqrt.age
## 1.3869 1.6797
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.17127429 0.34783633 0.89340469
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.64789914 0.63103004 0.15442436
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.76181953 0.38476380 0.55386245
## nodematch.race..wa.O absdiff.sqrt.age
## 0.16548182 0.09301537
## Joint P-value (lower = worse): 0.8846388 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.27343 0.47075 0.26208
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.27454 -0.34724 0.79363
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.14319 -0.09275 -1.00121
## nodematch.race..wa.O absdiff.sqrt.age
## -1.00861 -0.47683
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7845229 0.6378182 0.7932576
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.7836676 0.7284113 0.4274134
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.8861427 0.9261030 0.3167276
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3131636 0.6334858
## Joint P-value (lower = worse): 0.3837697 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.72014 0.91675 0.37306
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.72357 -0.09954 -0.66263
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.36336 -1.25520 -1.27408
## nodematch.race..wa.O absdiff.sqrt.age
## 0.48043 0.34133
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4714368 0.3592718 0.7091049
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.4693275 0.9207113 0.5075692
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.1727696 0.2094058 0.2026350
## nodematch.race..wa.O absdiff.sqrt.age
## 0.6309237 0.7328565
## Joint P-value (lower = worse): 0.4357829 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.55136 0.59264 0.64923
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.07306 -1.24957 0.61222
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.68118 1.22486 0.22372
## nodematch.race..wa.O absdiff.sqrt.age
## 0.51968 -1.59813
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.5813838 0.5534193 0.5161887
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.9417601 0.2114577 0.5403951
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.4957571 0.2206287 0.8229732
## nodematch.race..wa.O absdiff.sqrt.age
## 0.6032836 0.1100151
## Joint P-value (lower = worse): 0.3235072 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:750900000
## Thinning interval = 1e+05
## Number of chains = 4
## Sample size per chain = 7500
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.6674 28.719 0.16581 0.1819
## nodefactor.deg.pers.1 0.1559 17.576 0.10147 0.1109
## nodefactor.deg.pers.2 -1.1388 18.733 0.10816 0.1225
## nodefactor.race..wa.B 0.2557 12.532 0.07235 0.0848
## nodefactor.race..wa.H -0.6082 17.367 0.10027 0.1651
## nodefactor.region.EW 0.1618 17.769 0.10259 0.1490
## nodefactor.region.OW -0.2508 32.507 0.18768 0.2036
## nodematch.race..wa.B 0.1578 4.988 0.02880 0.0429
## nodematch.race..wa.H -0.4796 8.768 0.05062 0.1212
## nodematch.race..wa.O 0.8242 26.327 0.15200 0.1685
## absdiff.sqrt.age 0.4665 28.286 0.16331 0.1742
## nodematch.region 0.1894 29.297 0.16915 0.1904
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -55.500 -18.500 5.000e-01 20.500 56.500
## nodefactor.deg.pers.1 -34.000 -12.000 5.684e-14 12.000 35.000
## nodefactor.deg.pers.2 -37.000 -14.000 -1.000e+00 11.000 35.000
## nodefactor.race..wa.B -23.834 -7.834 1.664e-01 9.166 25.166
## nodefactor.race..wa.H -34.844 -11.844 -8.440e-01 11.156 33.156
## nodefactor.region.EW -34.561 -11.561 4.392e-01 12.439 34.439
## nodefactor.region.OW -64.131 -22.131 -1.306e-01 21.869 63.869
## nodematch.race..wa.B -9.177 -3.177 -1.769e-01 3.823 9.823
## nodematch.race..wa.H -17.300 -6.300 -3.003e-01 5.700 16.700
## nodematch.race..wa.O -50.946 -16.946 1.054e+00 19.054 52.054
## absdiff.sqrt.age -54.693 -18.678 3.171e-01 19.347 56.226
## nodematch.region -57.450 -19.450 -4.500e-01 19.550 57.550
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39882373
## nodefactor.deg.pers.1 0.3988237 1.00000000
## nodefactor.deg.pers.2 0.4253927 0.04655527
## nodefactor.race..wa.B 0.2543469 0.11224116
## nodefactor.race..wa.H 0.3522774 0.15028060
## nodefactor.region.EW 0.3272193 0.12794292
## nodefactor.region.OW 0.5760714 0.21740494
## nodematch.race..wa.B 0.1057674 0.05401513
## nodematch.race..wa.H 0.1276504 0.05001325
## nodematch.race..wa.O 0.8222503 0.31871374
## absdiff.sqrt.age 0.5447800 0.20999808
## nodematch.region 0.8767400 0.35326691
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42539265 0.25434693
## nodefactor.deg.pers.1 0.04655527 0.11224116
## nodefactor.deg.pers.2 1.00000000 0.12553490
## nodefactor.race..wa.B 0.12553490 1.00000000
## nodefactor.race..wa.H 0.16807656 0.04419428
## nodefactor.region.EW 0.15086021 0.02440577
## nodefactor.region.OW 0.22873429 0.11507395
## nodematch.race..wa.B 0.05478773 0.57149448
## nodematch.race..wa.H 0.07324067 -0.03950840
## nodematch.race..wa.O 0.33995698 -0.05523210
## absdiff.sqrt.age 0.23306318 0.13995024
## nodematch.region 0.36982364 0.22943954
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.352277377 0.32721929
## nodefactor.deg.pers.1 0.150280602 0.12794292
## nodefactor.deg.pers.2 0.168076555 0.15086021
## nodefactor.race..wa.B 0.044194283 0.02440577
## nodefactor.race..wa.H 1.000000000 0.24556499
## nodefactor.region.EW 0.245564986 1.00000000
## nodefactor.region.OW 0.189217659 0.01256993
## nodematch.race..wa.B -0.003062516 0.01471581
## nodematch.race..wa.H 0.598192290 0.13931629
## nodematch.race..wa.O -0.065845028 0.23580024
## absdiff.sqrt.age 0.194141292 0.16795694
## nodematch.region 0.303595438 0.23686343
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.57607137 0.105767360
## nodefactor.deg.pers.1 0.21740494 0.054015129
## nodefactor.deg.pers.2 0.22873429 0.054787727
## nodefactor.race..wa.B 0.11507395 0.571494482
## nodefactor.race..wa.H 0.18921766 -0.003062516
## nodefactor.region.EW 0.01256993 0.014715813
## nodefactor.region.OW 1.00000000 0.034407436
## nodematch.race..wa.B 0.03440744 1.000000000
## nodematch.race..wa.H 0.06539252 0.032055927
## nodematch.race..wa.O 0.48913232 0.015016380
## absdiff.sqrt.age 0.31245773 0.052566292
## nodematch.region 0.49090371 0.102124543
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.12765039 0.82225030
## nodefactor.deg.pers.1 0.05001325 0.31871374
## nodefactor.deg.pers.2 0.07324067 0.33995698
## nodefactor.race..wa.B -0.03950840 -0.05523210
## nodefactor.race..wa.H 0.59819229 -0.06584503
## nodefactor.region.EW 0.13931629 0.23580024
## nodefactor.region.OW 0.06539252 0.48913232
## nodematch.race..wa.B 0.03205593 0.01501638
## nodematch.race..wa.H 1.00000000 0.06833750
## nodematch.race..wa.O 0.06833750 1.00000000
## absdiff.sqrt.age 0.06123585 0.44211889
## nodematch.region 0.11351031 0.72161597
## absdiff.sqrt.age nodematch.region
## edges 0.54478003 0.8767400
## nodefactor.deg.pers.1 0.20999808 0.3532669
## nodefactor.deg.pers.2 0.23306318 0.3698236
## nodefactor.race..wa.B 0.13995024 0.2294395
## nodefactor.race..wa.H 0.19414129 0.3035954
## nodefactor.region.EW 0.16795694 0.2368634
## nodefactor.region.OW 0.31245773 0.4909037
## nodematch.race..wa.B 0.05256629 0.1021245
## nodematch.race..wa.H 0.06123585 0.1135103
## nodematch.race..wa.O 0.44211889 0.7216160
## absdiff.sqrt.age 1.00000000 0.4790433
## nodematch.region 0.47904330 1.0000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.06319881 0.042584481 0.07785070
## Lag 2e+05 0.02901302 0.047480914 0.02300900
## Lag 3e+05 0.02957396 0.017559286 0.01665821
## Lag 4e+05 0.03339324 -0.004741893 0.02759303
## Lag 5e+05 0.02172360 -0.003271898 0.01812706
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.1118295831 0.20460716 0.22671890
## Lag 2e+05 0.0468231260 0.12723742 0.11488121
## Lag 3e+05 0.0130329440 0.09454750 0.06397098
## Lag 4e+05 0.0109854321 0.08078360 0.04484796
## Lag 5e+05 -0.0008268687 0.06205369 0.02046217
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.070771600 0.329252257 0.5505790
## Lag 2e+05 0.009957765 0.149025650 0.3970296
## Lag 3e+05 0.024655149 0.070483496 0.2910314
## Lag 4e+05 0.019105841 0.024784916 0.2226306
## Lag 5e+05 0.021343673 0.009288153 0.1640154
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.08806448 0.058898532 0.095614874
## Lag 2e+05 0.02941725 0.019374321 0.047164713
## Lag 3e+05 0.02401473 0.018724482 0.045279657
## Lag 4e+05 0.01939618 0.009613618 0.029632498
## Lag 5e+05 0.01001271 -0.002262934 0.009240833
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.052379418 0.076003822 0.076860368
## Lag 2e+05 0.005765307 0.039212090 0.032646476
## Lag 3e+05 0.002331449 0.008994256 0.022003325
## Lag 4e+05 0.006942833 0.018363512 0.001016604
## Lag 5e+05 -0.008699272 0.007619185 0.002431188
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.103820773 0.22215297 0.23049924
## Lag 2e+05 0.039564130 0.14324235 0.10583693
## Lag 3e+05 0.025353703 0.08993701 0.04268079
## Lag 4e+05 0.007950401 0.06286781 0.02462168
## Lag 5e+05 -0.002109837 0.05969175 0.02807392
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.051972525 0.33115994 0.5506864
## Lag 2e+05 -0.014782457 0.14637483 0.3857423
## Lag 3e+05 0.009754428 0.09126691 0.2757427
## Lag 4e+05 0.011911817 0.05270805 0.2205706
## Lag 5e+05 -0.007504480 0.02147032 0.1892104
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.062345937 0.055122278 0.080214928
## Lag 2e+05 0.008739686 0.020940225 0.027256434
## Lag 3e+05 -0.001369010 0.011490805 0.003234749
## Lag 4e+05 0.014468063 -0.006033567 0.004398724
## Lag 5e+05 -0.018166102 0.006446410 -0.013199417
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.050490994 0.044537263 0.084113753
## Lag 2e+05 0.018789794 0.010959563 0.033040972
## Lag 3e+05 0.005876605 0.001727206 0.016975710
## Lag 4e+05 0.010477310 0.024201449 0.009136411
## Lag 5e+05 0.015358555 0.018882094 0.008408206
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.11557796 0.21432242 0.24115464
## Lag 2e+05 0.05130032 0.15373107 0.12612823
## Lag 3e+05 0.02744415 0.11271971 0.09335819
## Lag 4e+05 0.01548805 0.09403663 0.04582351
## Lag 5e+05 0.01140012 0.08150393 0.03813721
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.040277728 0.33768176 0.5381494
## Lag 2e+05 0.010837205 0.15275135 0.3842432
## Lag 3e+05 0.008247860 0.06316776 0.3040152
## Lag 4e+05 -0.001113046 0.04116602 0.2379443
## Lag 5e+05 0.028986054 0.01940220 0.1876097
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0617929383 0.054530046 0.066595706
## Lag 2e+05 0.0074123385 0.016204495 0.031566977
## Lag 3e+05 0.0006515408 0.002208614 0.015593173
## Lag 4e+05 0.0045486721 0.005560654 0.005262058
## Lag 5e+05 0.0109524308 0.006042239 0.023499797
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.058938044 0.091086164 0.077650217
## Lag 2e+05 0.041301746 0.026739394 0.037998745
## Lag 3e+05 -0.005129468 0.009130540 0.027620863
## Lag 4e+05 0.007709218 0.007673822 0.001098832
## Lag 5e+05 -0.009760663 0.008534006 0.014686789
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.133514131 0.21555913 0.21495067
## Lag 2e+05 0.072984982 0.14557414 0.11696602
## Lag 3e+05 0.026534158 0.10804051 0.06277538
## Lag 4e+05 0.015418246 0.08753610 0.03396595
## Lag 5e+05 -0.009185847 0.08665772 0.03876947
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.00000000 1.0000000
## Lag 1e+05 0.0463043805 0.33230131 0.5560083
## Lag 2e+05 0.0229144770 0.14936175 0.4112324
## Lag 3e+05 -0.0056222946 0.07214151 0.3096621
## Lag 4e+05 -0.0179520002 0.02874199 0.2377860
## Lag 5e+05 -0.0006059438 0.01928351 0.1924291
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.076224830 0.033193761 0.083881016
## Lag 2e+05 0.047461216 0.027590533 0.046255084
## Lag 3e+05 0.009418221 0.005059686 0.017409631
## Lag 4e+05 0.031811648 0.012856321 0.020960200
## Lag 5e+05 0.006959893 -0.005689170 0.006420059
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.9124 -0.4414 0.3485
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6907 -0.6099 0.7500
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -2.0339 1.4853 -1.2363
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -1.6250 0.3673 -0.7370
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.36154374 0.65890712 0.72748106
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.48973801 0.54195933 0.45326564
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.04195738 0.13746165 0.21633351
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.10417130 0.71342374 0.46114374
## Joint P-value (lower = worse): 0.1147244 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 1.1884 0.5781 0.8807
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.2619 0.8986 2.2547
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.0678 -0.7907 1.0749
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 1.4659 -0.4074 0.8391
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.23465944 0.56320956 0.37849606
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.79337070 0.36886153 0.02415261
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.28562255 0.42909614 0.28242800
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.14268622 0.68372391 0.40138684
## Joint P-value (lower = worse): 0.4359832 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0561 -0.3869 0.2332
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.0409 -0.6943 0.3325
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.6928 -0.5937 -0.5064
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.5123 0.4037 0.5283
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.29091814 0.69884542 0.81562757
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.29790232 0.48750681 0.73951277
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.09048624 0.55271232 0.61260592
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.60847016 0.68646954 0.59732331
## Joint P-value (lower = worse): 0.2496046 .
## Chain 4
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0081 -0.7275 -1.0099
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -2.2469 -0.6343 0.3251
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.3355 -2.7636 -1.3202
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## -0.7380 0.9269 -0.8632
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.313388155 0.466939175 0.312523915
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.024649387 0.525875683 0.745113623
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.181700411 0.005716529 0.186761457
## nodematch.race..wa.O absdiff.sqrt.age nodematch.region
## 0.460528190 0.353956081 0.388052612
## Joint P-value (lower = worse): 0.07631727 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.m.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x560796482160>
##
## Iterations: 163 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.74395 0.03417 0 <1e-04 ***
## deg2+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 2
summary(est.m.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5607ad31cec8>
##
## Iterations: 102 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.78864 0.03912 0 <1e-04 ***
## nodefactor.race..wa.B -0.38471 0.08766 0 <1e-04 ***
## nodefactor.race..wa.H 0.41133 0.06572 0 <1e-04 ***
## deg2+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 3
summary(est.m.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x5607c1d26e88>
##
## Iterations: 235 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.2727 0.1886 0 < 1e-04 ***
## nodefactor.race..wa.B 0.5925 0.1682 0 0.000426 ***
## nodefactor.race..wa.H 1.4058 0.1830 0 < 1e-04 ***
## nodematch.race..wa.B 1.3391 0.2618 0 < 1e-04 ***
## nodematch.race..wa.H 0.6582 0.2076 0 0.001525 **
## nodematch.race..wa.O 1.5656 0.1882 0 < 1e-04 ***
## deg2+ -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 4
summary(est.m.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x5607d6956978>
##
## Iterations: 152 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.17034 0.19083 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.30470 0.06226 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.09834 0.05912 0 0.09622 .
## nodefactor.race..wa.B 0.59675 0.16792 0 0.00038 ***
## nodefactor.race..wa.H 1.41065 0.18424 0 < 1e-04 ***
## nodematch.race..wa.B 1.33978 0.25705 0 < 1e-04 ***
## nodematch.race..wa.H 0.66351 0.20730 0 0.00137 **
## nodematch.race..wa.O 1.56433 0.18863 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 5
summary(est.m.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x5607eb6a3ed8>
##
## Iterations: 106 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.86114 0.19286 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31120 0.06253 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.10049 0.05977 0 0.092725 .
## nodefactor.race..wa.B 0.55488 0.16763 0 0.000932 ***
## nodefactor.race..wa.H 1.42475 0.18313 0 < 1e-04 ***
## nodefactor.region.EW -0.25065 0.06989 0 0.000335 ***
## nodefactor.region.OW -0.39516 0.04470 0 < 1e-04 ***
## nodematch.race..wa.B 1.34037 0.25998 0 < 1e-04 ***
## nodematch.race..wa.H 0.66006 0.20699 0 0.001428 **
## nodematch.race..wa.O 1.56595 0.18757 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 6
summary(est.m.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x560800505398>
##
## Iterations: 76 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.73835 0.19472 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31083 0.06328 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.10403 0.05972 0 0.08149 .
## nodefactor.race..wa.B 0.54964 0.16727 0 0.00102 **
## nodefactor.race..wa.H 1.42371 0.18384 0 < 1e-04 ***
## nodefactor.region.EW -0.24138 0.06966 0 0.00053 ***
## nodefactor.region.OW -0.39362 0.04476 0 < 1e-04 ***
## nodematch.race..wa.B 1.34183 0.25914 0 < 1e-04 ***
## nodematch.race..wa.H 0.66048 0.20759 0 0.00146 **
## nodematch.race..wa.O 1.56524 0.18773 0 < 1e-04 ***
## absdiff.sqrt.age -1.39760 0.04187 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 7
summary(est.m.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2)) +
## offset(nodemix("region", base = c(1, 3, 6)))
## <environment: 0x560815414bc8>
##
## Iterations: 59 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -8.315317 0.194012 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.310127 0.062656 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.104764 0.059749 0 0.079530 .
## nodefactor.race..wa.B 0.606938 0.168067 0 0.000305 ***
## nodefactor.race..wa.H 1.494859 0.183869 0 < 1e-04 ***
## nodefactor.region.EW 0.666595 0.060014 0 < 1e-04 ***
## nodefactor.region.OW -0.009103 0.037916 0 0.810263
## nodematch.race..wa.B 1.216911 0.260142 0 < 1e-04 ***
## nodematch.race..wa.H 0.509008 0.208100 0 0.014446 *
## nodematch.race..wa.O 1.627132 0.188217 0 < 1e-04 ***
## absdiff.sqrt.age -1.397501 0.041976 0 < 1e-04 ***
## deg2+ -Inf 0.000000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000000 0 < 1e-04 ***
## mix.region.EW.KC -Inf 0.000000 0 < 1e-04 ***
## mix.region.EW.OW -Inf 0.000000 0 < 1e-04 ***
## mix.region.KC.OW -Inf 0.000000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R mix.region.EW.KC mix.region.EW.OW mix.region.KC.OW
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 8
summary(est.m.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + nodematch("region",
## diff = FALSE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x56082a4104c0>
##
## Iterations: 87 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.03239 0.20677 0 < 1e-04 ***
## nodefactor.deg.pers.1 -0.31090 0.06278 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.10419 0.05970 0 0.080933 .
## nodefactor.race..wa.B 0.59298 0.16847 0 0.000432 ***
## nodefactor.race..wa.H 1.47743 0.18489 0 < 1e-04 ***
## nodefactor.region.EW 0.56263 0.06247 0 < 1e-04 ***
## nodefactor.region.OW -0.04204 0.03881 0 0.278705
## nodematch.race..wa.B 1.24035 0.26056 0 < 1e-04 ***
## nodematch.race..wa.H 0.54183 0.20893 0 0.009503 **
## nodematch.race..wa.O 1.61170 0.18837 0 < 1e-04 ***
## absdiff.sqrt.age -1.39797 0.04217 0 < 1e-04 ***
## nodematch.region 2.66475 0.07157 0 < 1e-04 ***
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Network diagnostics
Model 1
(dx_main1 <- netdx(est.m.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.5 2234.827 -0.003 31.719
## nodefactor.deg.pers.1 NA 572.696 NA 17.693
## nodefactor.deg.pers.2 NA 616.343 NA 18.859
## nodefactor.race..wa.B NA 272.185 NA 12.206
## nodefactor.race..wa.H NA 483.940 NA 16.751
## nodefactor.region.EW NA 453.268 NA 16.519
## nodefactor.region.OW NA 1462.782 NA 30.578
## nodematch.race..wa.B NA 8.232 NA 2.685
## nodematch.race..wa.H NA 26.348 NA 4.634
## nodematch.race..wa.O NA 1543.153 NA 26.422
## absdiff.sqrt.age NA 2547.696 NA 51.862
## nodematch.region NA 991.177 NA 26.059
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.703 -0.178 120.246
## Pct Edges Diss 0.007 0.007 0.003 0.002plot(dx_main1, type="formation")
plot(dx_main1, type="duration")
plot(dx_main1, type="dissolution")
Model 2
(dx_main2 <- netdx(est.m.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2242.452 0.001 28.395
## nodefactor.deg.pers.1 NA 574.868 NA 17.992
## nodefactor.deg.pers.2 NA 619.263 NA 18.074
## nodefactor.race..wa.B 213.834 216.734 0.014 12.680
## nodefactor.race..wa.H 587.844 589.952 0.004 15.627
## nodefactor.region.EW NA 469.565 NA 17.691
## nodefactor.region.OW NA 1469.677 NA 30.724
## nodematch.race..wa.B NA 5.244 NA 1.831
## nodematch.race..wa.H NA 36.657 NA 6.049
## nodematch.race..wa.O NA 1505.443 NA 25.217
## absdiff.sqrt.age NA 2548.298 NA 53.578
## nodematch.region NA 986.087 NA 26.643
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 126.149 -0.175 120.750
## Pct Edges Diss 0.007 0.007 -0.003 0.002plot(dx_main2, type="formation")
plot(dx_main2, type="duration")
plot(dx_main2, type="dissolution")
Model 3
(dx_main3 <- netdx(est.m.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2237.948 -0.001 30.837
## nodefactor.deg.pers.1 NA 569.327 NA 16.739
## nodefactor.deg.pers.2 NA 616.123 NA 19.700
## nodefactor.race..wa.B 213.834 212.875 -0.004 12.682
## nodefactor.race..wa.H 587.844 580.438 -0.013 17.648
## nodefactor.region.EW NA 466.720 NA 15.794
## nodefactor.region.OW NA 1468.944 NA 27.578
## nodematch.race..wa.B 31.177 31.688 0.016 5.116
## nodematch.race..wa.H 123.300 118.437 -0.039 9.352
## nodematch.race..wa.O 1638.946 1640.231 0.001 26.542
## absdiff.sqrt.age NA 2538.402 NA 49.974
## nodematch.region NA 986.999 NA 28.130
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 126.239 -0.175 121.093
## Pct Edges Diss 0.007 0.007 -0.003 0.002plot(dx_main3, type="formation")
plot(dx_main3, type="duration")
plot(dx_main3, type="dissolution")
Model 4
(dx_main4 <- netdx(est.m.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2234.744 -0.003 28.619
## nodefactor.deg.pers.1 493.000 494.521 0.003 18.906
## nodefactor.deg.pers.2 603.000 602.727 0.000 19.572
## nodefactor.race..wa.B 213.834 208.750 -0.024 12.620
## nodefactor.race..wa.H 587.844 580.989 -0.012 17.654
## nodefactor.region.EW NA 466.656 NA 16.416
## nodefactor.region.OW NA 1467.211 NA 30.367
## nodematch.race..wa.B 31.177 30.292 -0.028 5.077
## nodematch.race..wa.H 123.300 119.041 -0.035 8.643
## nodematch.race..wa.O 1638.946 1639.353 0.000 25.838
## absdiff.sqrt.age NA 2536.368 NA 45.469
## nodematch.region NA 983.803 NA 25.381
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.719 -0.178 120.412
## Pct Edges Diss 0.007 0.007 0.000 0.002plot(dx_main4, type="formation")
plot(dx_main4, type="duration")
plot(dx_main4, type="dissolution")
Model 5
(dx_main5 <- netdx(est.m.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2235.535 -0.002 28.357
## nodefactor.deg.pers.1 493.000 493.286 0.001 16.312
## nodefactor.deg.pers.2 603.000 600.457 -0.004 16.428
## nodefactor.race..wa.B 213.834 212.368 -0.007 12.291
## nodefactor.race..wa.H 587.844 579.359 -0.014 16.352
## nodefactor.region.EW 445.561 448.030 0.006 14.550
## nodefactor.region.OW 1278.131 1277.676 0.000 28.187
## nodematch.race..wa.B 31.177 31.341 0.005 4.909
## nodematch.race..wa.H 123.300 117.559 -0.047 9.629
## nodematch.race..wa.O 1638.946 1637.982 -0.001 26.233
## absdiff.sqrt.age NA 2546.125 NA 46.089
## nodematch.region NA 1046.027 NA 21.525
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.548 -0.179 120.292
## Pct Edges Diss 0.007 0.007 0.002 0.002plot(dx_main5, type="formation")
plot(dx_main5, type="duration")
plot(dx_main5, type="dissolution")
Model 6
(dx_main6 <- netdx(est.m.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.buildup.bal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2238.102 -0.001 28.109
## nodefactor.deg.pers.1 493.000 490.743 -0.005 16.184
## nodefactor.deg.pers.2 603.000 601.905 -0.002 18.063
## nodefactor.race..wa.B 213.834 214.122 0.001 12.458
## nodefactor.race..wa.H 587.844 573.825 -0.024 16.327
## nodefactor.region.EW 445.561 438.963 -0.015 14.526
## nodefactor.region.OW 1278.131 1283.462 0.004 28.911
## nodematch.race..wa.B 31.177 31.260 0.003 4.311
## nodematch.race..wa.H 123.300 112.323 -0.089 7.582
## nodematch.race..wa.O 1638.946 1639.690 0.000 25.723
## absdiff.sqrt.age 1206.285 1212.373 0.005 30.176
## nodematch.region NA 1059.746 NA 22.519
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 126.405 -0.174 120.702
## Pct Edges Diss 0.007 0.007 -0.004 0.002plot(dx_main6, type="formation")
plot(dx_main6, type="duration")
plot(dx_main6, type="dissolution")
Model 7
(dx_main7 <- netdx(est.m.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2205.330 -0.016 27.924
## nodefactor.deg.pers.1 493.000 489.719 -0.007 16.360
## nodefactor.deg.pers.2 603.000 593.763 -0.015 17.924
## nodefactor.race..wa.B 213.834 212.831 -0.005 12.175
## nodefactor.race..wa.H 587.844 543.241 -0.076 17.768
## nodefactor.region.EW 445.561 408.685 -0.083 18.079
## nodefactor.region.OW 1278.131 1266.092 -0.009 34.580
## nodematch.race..wa.B 31.177 30.025 -0.037 4.719
## nodematch.race..wa.H 123.300 92.966 -0.246 8.569
## nodematch.race..wa.O 1638.946 1617.693 -0.013 26.245
## absdiff.sqrt.age 1206.285 1213.921 0.006 25.013
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
## mix.region.EW.KC NA 0.000 NA 0.000
## mix.region.EW.OW NA 0.000 NA 0.000
## mix.region.KC.OW NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.720 -0.178 120.462
## Pct Edges Diss 0.007 0.007 -0.003 0.002plot(dx_main7, type="formation")
plot(dx_main7, type="duration")
plot(dx_main7, type="dissolution")
Model 8
(dx_main8 <- netdx(est.m.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2202.823 -0.017 30.321
## nodefactor.deg.pers.1 493.000 491.113 -0.004 18.044
## nodefactor.deg.pers.2 603.000 593.405 -0.016 17.964
## nodefactor.race..wa.B 213.834 210.918 -0.014 11.593
## nodefactor.race..wa.H 587.844 547.518 -0.069 18.066
## nodefactor.region.EW 445.561 404.939 -0.091 18.431
## nodefactor.region.OW 1278.131 1262.948 -0.012 30.986
## nodematch.race..wa.B 31.177 29.479 -0.054 4.739
## nodematch.race..wa.H 123.300 94.298 -0.235 7.839
## nodematch.race..wa.O 1638.946 1615.399 -0.014 26.837
## absdiff.sqrt.age 1206.285 1211.555 0.004 28.374
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
## mix.region.EW.KC NA 0.000 NA 0.000
## mix.region.EW.OW NA 0.000 NA 0.000
## mix.region.KC.OW NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.410 -0.180 120.179
## Pct Edges Diss 0.007 0.007 -0.001 0.002plot(dx_main8, type="formation")
plot(dx_main8, type="duration")
plot(dx_main8, type="dissolution")